Multimodal LLMs are increasingly used with multi-image, interleaved image-text inputs, yet hallucination evaluation remains largely single-image. We study target-binding hallucination: a model answers as if an object appears in the queried target image when it appears only elsewhere. We introduce a position-controlled COCO haystack with POPE-style object-existence queries and split negatives into clean cases (object absent everywhere) and distractor-present cases (absent from the target but present elsewhere). On InternVL2-8B at N = 10, clean false positives remain near floor (1.15%), whereas distractor-present cases reach 10.55% and show a position-sensitive profile; the same regime-level separation appears across three additional architectures and in an object-balanced PASCAL VOC2012 check. Channel and paired-removal controls are consistent with a visual-channel-dominant source-misattribution interpretation, and a related MIHBench Existence re-audit finds that over 98% of false positives concentrate in partial-present negatives. For object-existence diagnostics and other source-sensitive multi-image evaluations with auditable negative evidence, we recommend reporting clean and evidence-supported negatives separately whenever non-target images may contain supporting evidence.